{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 第一步： 载入数据\n",
    "\n",
    "1. Airbnb的房屋信息数据：\n",
    "   1. price_per_person：人均价格\n",
    "   2. overall_satisfaction：总体满意程度\n",
    "   3. N: Number_of_listings： 第三个指标\n",
    "2. 根据上面3个指标进行聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>price_per_person</th>\n",
       "      <th>overall_satisfaction</th>\n",
       "      <th>N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALLEGHENY_WEST</td>\n",
       "      <td>120.791667</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BELLA_VISTA</td>\n",
       "      <td>87.407920</td>\n",
       "      <td>3.158333</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BELMONT</td>\n",
       "      <td>69.425000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BREWERYTOWN</td>\n",
       "      <td>71.788188</td>\n",
       "      <td>1.943182</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BUSTLETON</td>\n",
       "      <td>55.833333</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     neighborhood  price_per_person  overall_satisfaction    N\n",
       "0  ALLEGHENY_WEST        120.791667              4.666667   23\n",
       "1     BELLA_VISTA         87.407920              3.158333  204\n",
       "2         BELMONT         69.425000              3.250000   11\n",
       "3     BREWERYTOWN         71.788188              1.943182  142\n",
       "4       BUSTLETON         55.833333              1.250000   19"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airbnb = pd.read_csv(\"data/data.csv\")\n",
    "airbnb.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 第二步: 进行聚类\n",
    "\n",
    "1. 三个特征：price_per_person， overall_satisfaction, N\n",
    "2. 决定聚类数（5个cluster）,这个数字怎么确定（调惨），最后再说；\n",
    "3. 标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>price_per_person</th>\n",
       "      <th>overall_satisfaction</th>\n",
       "      <th>N</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALLEGHENY_WEST</td>\n",
       "      <td>120.791667</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BELLA_VISTA</td>\n",
       "      <td>87.407920</td>\n",
       "      <td>3.158333</td>\n",
       "      <td>204</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BELMONT</td>\n",
       "      <td>69.425000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BREWERYTOWN</td>\n",
       "      <td>71.788188</td>\n",
       "      <td>1.943182</td>\n",
       "      <td>142</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BUSTLETON</td>\n",
       "      <td>55.833333</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     neighborhood  price_per_person  overall_satisfaction    N  label\n",
       "0  ALLEGHENY_WEST        120.791667              4.666667   23      2\n",
       "1     BELLA_VISTA         87.407920              3.158333  204      2\n",
       "2         BELMONT         69.425000              3.250000   11      2\n",
       "3     BREWERYTOWN         71.788188              1.943182  142      1\n",
       "4       BUSTLETON         55.833333              1.250000   19      1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 初始化Kmeans对象\n",
    "kmeans = KMeans(n_clusters=5, random_state=42)\n",
    "\n",
    "# 标准化数据\n",
    "scaler = StandardScaler()\n",
    "\n",
    "scaled_airbnb_data = scaler.fit_transform(\n",
    "    airbnb[['price_per_person', 'overall_satisfaction', 'N']])\n",
    "\n",
    "# 开始运行！\n",
    "kmeans.fit(scaled_airbnb_data)\n",
    "\n",
    "# 保存聚类标记\n",
    "airbnb['label'] = kmeans.labels_\n",
    "\n",
    "airbnb.head()\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 第三步: 计算每个聚类的平均特征(feature)\n",
    "\n",
    "为了更加深入了解每个聚类，在计算K-means后，进行：\n",
    "\n",
    "1. 根据 `label` 列分组;\n",
    "2. 计算每个特征的均值 `mean()`;\n",
    "3. 计算每个聚类的邻居数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  size\n",
       "0      0    21\n",
       "1      1    23\n",
       "2      2    59\n",
       "3      3     1\n",
       "4      4     1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airbnb.groupby('label', as_index=False).size() \n",
    "# 展示数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/wp/zfkgqzvj2lq6fk2gy0rbmq6m0000gn/T/ipykernel_87202/4275791276.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  airbnb.groupby('label', as_index = \\\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>price_per_person</th>\n",
       "      <th>overall_satisfaction</th>\n",
       "      <th>N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>67.526755</td>\n",
       "      <td>3.232236</td>\n",
       "      <td>69.864407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>78.935232</td>\n",
       "      <td>0.920238</td>\n",
       "      <td>38.347826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>119.329173</td>\n",
       "      <td>2.947605</td>\n",
       "      <td>313.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>136.263996</td>\n",
       "      <td>3.000924</td>\n",
       "      <td>1499.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>387.626984</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  price_per_person  overall_satisfaction            N\n",
       "2      2         67.526755              3.232236    69.864407\n",
       "1      1         78.935232              0.920238    38.347826\n",
       "0      0        119.329173              2.947605   313.000000\n",
       "4      4        136.263996              3.000924  1499.000000\n",
       "3      3        387.626984              5.000000    31.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airbnb.groupby('label', as_index = \\\n",
    "    False).mean().sort_values(by='price_per_person')\n",
    "# 计算特征的均值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 第四步：根据聚类结果可视化\n",
    "\n",
    "1. 载入地理信息数据\n",
    "2. 把Airbnb数据和邻居的数据合并\n",
    "3. 用geopandas绘制邻居"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-8381967.050521846, -8342231.252889295, 4844665.404724952, 4887984.248160506)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hoods = gpd.read_file(\"./data/geodata.geojson\")\n",
    "airbnb2 = hoods.merge(airbnb, left_on='Name', \\\n",
    "                      right_on='neighborhood', how='left')\n",
    "airbnb2['label'] = airbnb2['label'].fillna(-1)\n",
    "airbnb2 = airbnb2.to_crs(epsg=3857)\n",
    "# 设置图片\n",
    "f, ax = plt.subplots(figsize=(6, 8))\n",
    "# 绘制、按照label上色\n",
    "# 确定类别数据、增加标注\n",
    "airbnb2.plot(column=\"label\",cmap=\"Dark2\",\n",
    "    categorical=True,legend=True,edgecolor=\"k\",\n",
    "    lw=0.5,ax=ax,)\n",
    "ax.set_axis_off()\n",
    "plt.axis(\"equal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 附录：如何确定参数K？\n",
    "\n",
    "1. 肘方法！\n",
    "2. K-folder检验！\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_clusters = list(range(2, 10))\n",
    "inertias = []\n",
    "for k in n_clusters:\n",
    "\n",
    "    kmeans = KMeans(n_clusters=k)\n",
    "    kmeans.fit(scaled_airbnb_data)\n",
    "    inertias.append(kmeans.inertia_)\n",
    "    \n",
    "# Plot it!\n",
    "plt.plot(n_clusters, inertias, marker='o',\\\n",
    "        ms=10, mfc='white', lw=4, mew=3)\n",
    "\n",
    "# 到5左右下降一直都很快：选择5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最好分成5个聚类。\n"
     ]
    }
   ],
   "source": [
    "from kneed import KneeLocator\n",
    "# 用kneed包！\n",
    "\n",
    "kn = KneeLocator(n_clusters, inertias, curve='convex', direction='decreasing')\n",
    "\n",
    "print(f\"最好分成{kn.knee}个聚类。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py38",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
